Overview

Dataset statistics

Number of variables14
Number of observations342645
Missing cells54374
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.6 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 54374 (15.9%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.42121827)Skewed

Reproduction

Analysis started2023-10-10 11:27:50.952755
Analysis finished2023-10-10 11:28:20.505269
Duration29.55 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1.0
342645 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1027935
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 342645
100.0%

Length

2023-10-10T11:28:20.663534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T11:28:20.889452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 342645
100.0%

Most occurring characters

ValueCountFrequency (%)
1 342645
33.3%
. 342645
33.3%
0 342645
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 685290
66.7%
Other Punctuation 342645
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 342645
50.0%
0 342645
50.0%
Other Punctuation
ValueCountFrequency (%)
. 342645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1027935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 342645
33.3%
. 342645
33.3%
0 342645
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1027935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 342645
33.3%
. 342645
33.3%
0 342645
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-09-10 00:00:00
Maximum2023-09-10 00:00:00
2023-10-10T11:28:21.083081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:21.301867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2023-09-01 00:00:00
Maximum2023-09-01 00:00:00
2023-10-10T11:28:21.503050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:21.728453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-10-10T11:28:22.142057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:22.388777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.77818
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:22.679410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1034.1893
Coefficient of variation (CV)2.2993319
Kurtosis55.278205
Mean449.77818
Median Absolute Deviation (MAD)8
Skewness7.5633415
Sum1.5411424 × 108
Variance1069547.5
MonotonicityNot monotonic
2023-10-10T11:28:23.027943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 48065
14.0%
313 44540
13.0%
303 39400
11.5%
330 27082
 
7.9%
316 23277
 
6.8%
308 18412
 
5.4%
306 14375
 
4.2%
324 14059
 
4.1%
301 13757
 
4.0%
304 11175
 
3.3%
Other values (18) 88503
25.8%
ValueCountFrequency (%)
301 13757
 
4.0%
302 7534
 
2.2%
303 39400
11.5%
304 11175
 
3.3%
305 48065
14.0%
306 14375
 
4.2%
307 6029
 
1.8%
308 18412
 
5.4%
310 3760
 
1.1%
313 44540
13.0%
ValueCountFrequency (%)
8418 4650
 
1.4%
8416 1017
 
0.3%
1900 228
 
0.1%
390 948
 
0.3%
389 3599
 
1.1%
362 4415
 
1.3%
361 2472
 
0.7%
335 3472
 
1.0%
330 27082
7.9%
329 899
 
0.3%
Distinct1901
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:23.697264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3522275
Min length2

Characters and Unicode

Total characters1148624
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row99
2nd row14
3rd row02
4th row05
5th row13
ValueCountFrequency (%)
101 1460
 
0.4%
402 1412
 
0.4%
301 1384
 
0.4%
403 1379
 
0.4%
201 1304
 
0.4%
203 1282
 
0.4%
401 1155
 
0.3%
404 1141
 
0.3%
409 1112
 
0.3%
802 1108
 
0.3%
Other values (1891) 329908
96.3%
2023-10-10T11:28:24.725980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 219569
19.1%
0 211086
18.4%
2 152259
13.3%
3 124332
10.8%
5 88527
7.7%
9 82753
 
7.2%
4 81406
 
7.1%
7 67627
 
5.9%
6 59977
 
5.2%
8 49455
 
4.3%
Other values (15) 11633
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1136991
99.0%
Uppercase Letter 11633
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2175
18.7%
M 1950
16.8%
B 1413
12.1%
Z 1003
8.6%
E 980
8.4%
D 765
 
6.6%
A 754
 
6.5%
F 725
 
6.2%
C 380
 
3.3%
K 377
 
3.2%
Other values (5) 1111
9.6%
Decimal Number
ValueCountFrequency (%)
1 219569
19.3%
0 211086
18.6%
2 152259
13.4%
3 124332
10.9%
5 88527
7.8%
9 82753
 
7.3%
4 81406
 
7.2%
7 67627
 
5.9%
6 59977
 
5.3%
8 49455
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1136991
99.0%
Latin 11633
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2175
18.7%
M 1950
16.8%
B 1413
12.1%
Z 1003
8.6%
E 980
8.4%
D 765
 
6.6%
A 754
 
6.5%
F 725
 
6.2%
C 380
 
3.3%
K 377
 
3.2%
Other values (5) 1111
9.6%
Common
ValueCountFrequency (%)
1 219569
19.3%
0 211086
18.6%
2 152259
13.4%
3 124332
10.9%
5 88527
7.8%
9 82753
 
7.3%
4 81406
 
7.2%
7 67627
 
5.9%
6 59977
 
5.3%
8 49455
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1148624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 219569
19.1%
0 211086
18.4%
2 152259
13.3%
3 124332
10.8%
5 88527
7.7%
9 82753
 
7.2%
4 81406
 
7.1%
7 67627
 
5.9%
6 59977
 
5.2%
8 49455
 
4.3%
Other values (15) 11633
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6179
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4033343 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:25.092770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799058
median1.4959903 × 108
Q39.90004 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020494 × 108

Descriptive statistics

Standard deviation4.2887002 × 108
Coefficient of variation (CV)0.97396651
Kurtosis-1.7351762
Mean4.4033343 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.46986069
Sum1.5087805 × 1014
Variance1.8392949 × 1017
MonotonicityNot monotonic
2023-10-10T11:28:25.451369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2526
 
0.7%
990004007 2474
 
0.7%
990003004 2353
 
0.7%
990004006 1988
 
0.6%
990356076 1830
 
0.5%
990356073 1690
 
0.5%
131999228 1680
 
0.5%
131999164 1656
 
0.5%
990003007 1534
 
0.4%
131999194 1502
 
0.4%
Other values (6169) 323412
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 170
< 0.1%
998418080 154
< 0.1%
998418079 39
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 238
0.1%
998418073 240
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10395
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510.68185
Minimum1
Maximum165184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:25.785807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3101
95-th percentile1718
Maximum165184
Range165183
Interquartile range (IQR)98

Descriptive statistics

Standard deviation3185.634
Coefficient of variation (CV)6.2380012
Kurtosis414.71829
Mean510.68185
Median Absolute Deviation (MAD)12
Skewness16.863148
Sum1.7498258 × 108
Variance10148264
MonotonicityNot monotonic
2023-10-10T11:28:26.137487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57100
 
16.7%
2 27817
 
8.1%
3 18204
 
5.3%
4 13252
 
3.9%
5 10445
 
3.0%
6 8789
 
2.6%
7 7339
 
2.1%
8 6160
 
1.8%
9 5639
 
1.6%
10 5024
 
1.5%
Other values (10385) 182876
53.4%
ValueCountFrequency (%)
1 57100
16.7%
2 27817
8.1%
3 18204
 
5.3%
4 13252
 
3.9%
5 10445
 
3.0%
6 8789
 
2.6%
7 7339
 
2.1%
8 6160
 
1.8%
9 5639
 
1.6%
10 5024
 
1.5%
ValueCountFrequency (%)
165184 1
< 0.1%
162459 1
< 0.1%
156552 1
< 0.1%
155870 1
< 0.1%
154483 1
< 0.1%
154259 1
< 0.1%
144715 1
< 0.1%
118396 1
< 0.1%
115935 1
< 0.1%
113246 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11168
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.42535
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:26.475555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3110
95-th percentile1963
Maximum240002
Range240001
Interquartile range (IQR)107

Descriptive statistics

Standard deviation4111.3164
Coefficient of variation (CV)6.79079
Kurtosis726.93634
Mean605.42535
Median Absolute Deviation (MAD)13
Skewness21.421218
Sum2.0744597 × 108
Variance16902923
MonotonicityNot monotonic
2023-10-10T11:28:26.816239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55010
 
16.1%
2 27342
 
8.0%
3 18023
 
5.3%
4 13043
 
3.8%
5 10370
 
3.0%
6 8761
 
2.6%
7 7270
 
2.1%
8 6107
 
1.8%
9 5567
 
1.6%
10 5018
 
1.5%
Other values (11158) 186134
54.3%
ValueCountFrequency (%)
1 55010
16.1%
2 27342
8.0%
3 18023
 
5.3%
4 13043
 
3.8%
5 10370
 
3.0%
6 8761
 
2.6%
7 7270
 
2.1%
8 6107
 
1.8%
9 5567
 
1.6%
10 5018
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230940 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226321 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215132 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9294
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7653.6205
Minimum1
Maximum230640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:27.147277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1384
median1675
Q36204
95-th percentile36749
Maximum230640
Range230639
Interquartile range (IQR)5820

Descriptive statistics

Standard deviation17939.359
Coefficient of variation (CV)2.343905
Kurtosis34.83263
Mean7653.6205
Median Absolute Deviation (MAD)1534
Skewness5.1117917
Sum2.6224748 × 109
Variance3.2182061 × 108
MonotonicityNot monotonic
2023-10-10T11:28:27.478352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 583
 
0.2%
8 565
 
0.2%
9 542
 
0.2%
19 510
 
0.1%
12 505
 
0.1%
26 503
 
0.1%
25 501
 
0.1%
15 497
 
0.1%
14 496
 
0.1%
5 496
 
0.1%
Other values (9284) 337447
98.5%
ValueCountFrequency (%)
1 442
0.1%
2 487
0.1%
3 474
0.1%
4 490
0.1%
5 496
0.1%
6 457
0.1%
7 493
0.1%
8 565
0.2%
9 542
0.2%
10 436
0.1%
ValueCountFrequency (%)
230640 23
< 0.1%
227999 23
< 0.1%
227310 19
< 0.1%
218429 24
< 0.1%
214507 17
< 0.1%
213516 25
< 0.1%
211579 17
< 0.1%
210416 19
< 0.1%
205338 17
< 0.1%
200600 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10393
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11104.744
Minimum1
Maximum370140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:27.815523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1507
median2333
Q39025
95-th percentile52238
Maximum370140
Range370139
Interquartile range (IQR)8518

Descriptive statistics

Standard deviation26920.999
Coefficient of variation (CV)2.4242791
Kurtosis38.107923
Mean11104.744
Median Absolute Deviation (MAD)2152
Skewness5.3410221
Sum3.804985 × 109
Variance7.2474018 × 108
MonotonicityNot monotonic
2023-10-10T11:28:28.188779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 430
 
0.1%
11 421
 
0.1%
25 421
 
0.1%
13 417
 
0.1%
10 416
 
0.1%
8 416
 
0.1%
23 415
 
0.1%
33 406
 
0.1%
3 402
 
0.1%
5 401
 
0.1%
Other values (10383) 338500
98.8%
ValueCountFrequency (%)
1 361
0.1%
2 377
0.1%
3 402
0.1%
4 430
0.1%
5 401
0.1%
6 396
0.1%
7 389
0.1%
8 416
0.1%
9 342
0.1%
10 416
0.1%
ValueCountFrequency (%)
370140 23
< 0.1%
365354 23
< 0.1%
348485 25
< 0.1%
346189 19
< 0.1%
344333 24
< 0.1%
341653 19
< 0.1%
323756 20
< 0.1%
315771 17
< 0.1%
310754 17
< 0.1%
298627 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean665442.76
Minimum1610
Maximum1487634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:28.530440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile40186
Q1248733
median765014
Q31026304
95-th percentile1332331
Maximum1487634
Range1486024
Interquartile range (IQR)777571

Descriptive statistics

Standard deviation422451.07
Coefficient of variation (CV)0.63484209
Kurtosis-1.1950283
Mean665442.76
Median Absolute Deviation (MAD)318423
Skewness-0.011052742
Sum2.2801063 × 1011
Variance1.7846491 × 1011
MonotonicityNot monotonic
2023-10-10T11:28:28.882190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880929 5102
 
1.5%
874090 4354
 
1.3%
843974 4347
 
1.3%
894306 4333
 
1.3%
880469 4273
 
1.2%
897702 4212
 
1.2%
765014 4089
 
1.2%
803539 4027
 
1.2%
775457 3978
 
1.2%
1080758 3890
 
1.1%
Other values (315) 300040
87.6%
ValueCountFrequency (%)
1610 130
< 0.1%
1829 138
< 0.1%
1920 131
< 0.1%
2032 72
 
< 0.1%
2495 173
0.1%
2520 190
0.1%
2592 62
 
< 0.1%
3737 279
0.1%
4096 166
< 0.1%
4765 80
 
< 0.1%
ValueCountFrequency (%)
1487634 2975
0.9%
1450392 3048
0.9%
1421709 3564
1.0%
1344286 3543
1.0%
1340600 3441
1.0%
1332331 3545
1.0%
1316381 3463
1.0%
1282937 3576
1.0%
1267090 3350
1.0%
1265242 1177
 
0.3%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081195
Minimum1861
Maximum2664439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:29.248259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46325
Q1356291
median1106921
Q31776680
95-th percentile2548284
Maximum2664439
Range2662578
Interquartile range (IQR)1420389

Descriptive statistics

Standard deviation757875.73
Coefficient of variation (CV)0.70096118
Kurtosis-0.85544451
Mean1081195
Median Absolute Deviation (MAD)703633
Skewness0.34368238
Sum3.7046606 × 1011
Variance5.7437562 × 1011
MonotonicityNot monotonic
2023-10-10T11:28:29.612254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211794 5102
 
1.5%
1281484 4354
 
1.3%
1216251 4347
 
1.3%
1315569 4333
 
1.3%
1300433 4273
 
1.2%
1341828 4212
 
1.2%
1155933 4089
 
1.2%
1205468 4027
 
1.2%
1143059 3978
 
1.2%
2548284 3890
 
1.1%
Other values (315) 300040
87.6%
ValueCountFrequency (%)
1861 130
< 0.1%
2090 72
 
< 0.1%
2097 138
< 0.1%
2195 131
< 0.1%
2684 62
 
< 0.1%
2816 173
0.1%
3288 190
0.1%
3742 279
0.1%
4766 80
 
< 0.1%
4897 166
< 0.1%
ValueCountFrequency (%)
2664439 3866
1.1%
2663479 3793
1.1%
2618839 3789
1.1%
2593690 3844
1.1%
2548284 3890
1.1%
2480054 3851
1.1%
2469115 3880
1.1%
2178571 3757
1.1%
2062191 3811
1.1%
2052156 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3618
Distinct (%)1.3%
Missing54374
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean3572.0718
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-10-10T11:28:30.156105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1475
median1235
Q34140
95-th percentile13520
Maximum287220
Range287150
Interquartile range (IQR)3665

Descriptive statistics

Standard deviation6515.0513
Coefficient of variation (CV)1.8238859
Kurtosis140.86348
Mean3572.0718
Median Absolute Deviation (MAD)1010
Skewness7.070028
Sum1.0297247 × 109
Variance42445893
MonotonicityNot monotonic
2023-10-10T11:28:30.507251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2149
 
0.6%
105 1965
 
0.6%
185 1800
 
0.5%
110 1791
 
0.5%
180 1619
 
0.5%
175 1501
 
0.4%
140 1493
 
0.4%
300 1429
 
0.4%
145 1371
 
0.4%
125 1371
 
0.4%
Other values (3608) 271782
79.3%
(Missing) 54374
 
15.9%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 664
 
0.2%
95 734
 
0.2%
100 1012
0.3%
105 1965
0.6%
110 1791
0.5%
115 1128
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99590 5
< 0.1%

Interactions

2023-10-10T11:28:16.232966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:57.299118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:59.817487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:02.118602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:04.398051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:06.670338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:08.921618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:11.481724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:13.859417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:16.506448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:57.577080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:00.089225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:02.399468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:04.663429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:06.930702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:09.199203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:11.764775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:14.129673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:16.761566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:57.842297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:00.338611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:02.643208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:04.914914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:07.184715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:09.666307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:12.019753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:14.379972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:17.020803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:58.244719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:00.601017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:02.896558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:05.161922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:07.434107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:09.935347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:12.284698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:14.638436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:17.276590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:58.500855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:00.846441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:03.138797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:05.406283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:07.676589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:10.191046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:12.540950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:14.887574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:17.517755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:58.746309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:01.092176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:03.375457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:05.640959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:07.905087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:10.424142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:12.791435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:15.129878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:17.793239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:59.020882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:01.355046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:03.629221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:05.902400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:08.157181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:10.686768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:13.060831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:15.404506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:18.063854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:59.296982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:01.624478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:03.899604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:06.164585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:08.422235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:10.960016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:13.331154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:15.691739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:18.315714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:27:59.556707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:01.868291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:04.144391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:06.422456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:08.671596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:11.218174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:13.602029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-10T11:28:15.973423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-10T11:28:30.743669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2150.0070.012-0.061-0.055-0.536-0.4580.051
ZORGPRODUCT_CD0.2151.000-0.139-0.147-0.175-0.205-0.359-0.3870.028
AANTAL_PAT_PER_ZPD0.007-0.1391.0000.9960.3280.3260.0870.094-0.297
AANTAL_SUBTRAJECT_PER_ZPD0.012-0.1470.9961.0000.3250.3270.0900.102-0.300
AANTAL_PAT_PER_DIAG-0.061-0.1750.3280.3251.0000.9880.3460.3280.034
AANTAL_SUBTRAJECT_PER_DIAG-0.055-0.2050.3260.3270.9881.0000.3620.3600.043
AANTAL_PAT_PER_SPC-0.536-0.3590.0870.0900.3460.3621.0000.963-0.004
AANTAL_SUBTRAJECT_PER_SPC-0.458-0.3870.0940.1020.3280.3600.9631.000-0.005
GEMIDDELDE_VERKOOPPRIJS0.0510.028-0.297-0.3000.0340.043-0.004-0.0051.000

Missing values

2023-10-10T11:28:18.700788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T11:28:19.498051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-09-102023-09-012018-01-0132999990029011141141370838482198124168545.0
11.02023-09-102023-09-012018-01-013291499002901044979721981241681345.0
21.02023-09-102023-09-012018-01-013290299002901113751386487249772198124168545.0
31.02023-09-102023-09-012018-01-01329059900290102562611285134621981241681345.0
41.02023-09-102023-09-012018-01-0132913990029002661561572198124168205.0
51.02023-09-102023-09-012018-01-0132907990029011676690143915002198124168545.0
61.02023-09-102023-09-012018-01-013291959899067111215228521981241681440.0
71.02023-09-102023-09-012018-01-0132912990029012343411412021981241681040.0
81.02023-09-102023-09-012018-01-0132914990029011616197972198124168545.0
91.02023-09-102023-09-012018-01-0132910990029002141493992198124168205.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3426351.02023-09-102023-09-012014-01-013034139979900833217242142170918456061975.0
3426361.02023-09-102023-09-012020-01-01320804979001219222293340323103099418310988335.0
3426371.02023-09-102023-09-012020-01-013138422949900422502222342104704826188399110.0
3426381.02023-09-102023-09-012014-01-0130333529199031111284817619142170918456064125.0
3426391.02023-09-102023-09-012020-01-01313020131999154223001380510470482618839965.0
3426401.02023-09-102023-09-012020-01-013134347979901111342494104704826188392455.0
3426411.02023-09-102023-09-012014-01-0130333429199075117963987914217091845606630.0
3426421.02023-09-102023-09-012014-01-013031311319991002219202189142170918456063605.0
3426431.02023-09-102023-09-012014-01-0130324919929906711884958142170918456062470.0
3426441.02023-09-102023-09-012016-01-01307M132010805611325962677013491185655NaN